计算点与点之间的距离的平局距离

doublecomputeCloudResolution (const pcl::PointCloud
     
      ::ConstPtr &cloud){  double res = 0.0;  int n_points = 0;  int nres;  std::vector
      
        indices (2);  std::vector
       
         sqr_distances (2);  pcl::search::KdTree
        
          tree;  tree.setInputCloud (cloud);  for (size_t i = 0; i < cloud->size (); ++i)  {    if (! pcl_isfinite ((*cloud)[i].x))    {      continue;    }    //Considering the second neighbor since the first is the point itself.    nres = tree.nearestKSearch (i, 2, indices, sqr_distances);    if (nres == 2)    {      res += sqrt (sqr_distances[1]);      ++n_points;    }  }  if (n_points != 0)  {    res /= n_points;  }  return res;}
        
       
      
     

PCL中

对于 pcl::UniformSampling函数在PCL1.7版本里  该函数放在keypoints 用法如下： pcl::PointCloud
    
      keypointIndices; filter.compute(keypointIndices); pcl::copyPointCloud(*cloud,, *filteredCloud); 之后在PCL1.8版本里就将该函数放在filters模块里。并在keypoints模块里也包含了这个头文件 #warning UniformSampling is not a Keypoint anymore, use 
     
       instead. 这是keypoints模块下的说明 用法是：   PointCloud
      
        output_;   (output_);